Glaucoma Suspects: The Impact of Risk Factor-Driven Review Periods on Clinical Load, Diagnoses, and Healthcare Costs

Abstract

Purpose To model the healthcare impact (clinical attendance time and financial cost) and clinical outcomes (glaucoma diagnoses) of different risk factor–driven review frequencies for glaucoma suspect patients up until the point of discharge or diagnosis. Methods Medical records of 494 glaucoma suspects were examined to extract the clinical diagnosis. Two criteria for review periods were defined, based on contrasting stringency from established clinical guidelines: American Academy of Ophthalmology (AAO), more stringent/less frequent; and the Australian National Health and Medical Research Council (NHMRC), less stringent/more frequent. We used these data to model patient outcomes and healthcare costs using a Markov model. Results The less stringent/more frequent criterion resulted in more high-risk glaucoma suspects requiring more frequent review compared with the more stringent/less frequent criterion. Across the 15 Markov cycles (7.5 years), the less stringent/more frequent review criterion resulted in 6.6% more diagnoses and fewer overall clinical visits (14.7%) and reduced cost per diagnosis by 12% to 32% ( P < 0.0001). The number of glaucoma diagnoses made using each criterion converged at 2.5 to 3 years. Conclusions The stringency of risk assessments for glaucoma suspects impacts review periods and therefore clinical load, healthcare costs, and diagnosis rates. Using current testing methods, more frequent review periods appear advantageous for diagnostic efficiency, with both lower clinic load and lower cost up until the point of discharge or glaucoma diagnosis. Translational Relevance A less stringent criterion for assessing the risk of developing glaucoma potentially offers a more cost-effective method for reviewing glaucoma suspects, especially within the first 2.5 years.

Systematic review and appraisal of quality, definitions and treatment recommendations of clinical guidelines for glaucoma suspects

“…Such an increased risk might be present in an individual without definitive evidence for either the absence or presence of glaucoma [4][5][6][7] (meaning that a subset may actually have glaucoma that will become definitively diagnosed subsequently) and/or an individual with risk factors for glaucoma development. [8][9][10][11][12][13] The robust characterisation of such risk is foundational to the criteria used to define a POAG suspect, and how they should be managed. However, the NICE guideline for glaucoma 41 concluded that there was insufficient rigorous evidence to recommend a prognostic tool to accurately identify individuals at an increased risk of developing glaucoma, based on a critical review of the quality of evidence.…”

“…[4][5][6][7] However, this terminology has also often been used to include individuals with characteristics deemed to be associated with a higher risk of developing glaucoma, such as elevated intraocular pressure. [8][9][10][11][12][13] There are substantially more individuals deemed to be glaucoma suspects than those with definite glaucoma, 14,15 and these individuals form a notable proportion of those under clinical care with a glaucoma-related diagnosis. [16][17][18] High-quality clinical practice guidelines that provide evidence-based guidance on the management of glaucoma suspects might therefore be expected to contribute to minimising the global impact of glaucoma.…”

“…Consequently, the term ‘glaucoma suspect’ has been used to describe individuals with clinical findings that may be indicative of glaucomatous damage, but who cannot be definitively diagnosed as having glaucoma 4–7 . However, this terminology has also often been used to include individuals with characteristics deemed to be associated with a higher risk of developing glaucoma, such as elevated intraocular pressure 8–13 …”

Frontloading visual field tests detect earlier mean deviation progression when applied to real‐world‐derived early‐stage glaucoma data

“…This study focussed on MD as the index of glaucoma progression and beneficial outcome when comparing methods, but this can be further examined using other metrics such as quality-of-life, visual functioning 35 and financial cost, 36,37 which have also been linked to different progression strata. While we have previously shown that the absolute cost in time is minimal at the level of the individual patient, over time and for many patients, this cost is expected to accumulate.…”

Clinical Perspectives on the Use of Computer Vision in Glaucoma Screening

“…Deep learning (DL) is a subset of artificial intelligence (AI) based on neural networks that use an active learning strategy in the automated detection of glaucoma based on fundus images [ 36 , 37 ]. It can recognize patterns with glaucomatous features in images quickly and accurately [ 8 ], achieving a robust performance in detecting other retinal pathologies such as diabetic retinopathy and retinopathy of prematurity, macular edema, and age-related macular degeneration [ 32 ], with the potential to assist specialists in mass screening of glaucoma [ 38 ], reducing costs, and offering the potential to solve complex problems involving large datasets with medical images and classify diseases with a good innovative perspective for the introduction of individualized medicine and the optimization of diagnosis and therapy, screening, and prognosis [ 22 , 39 , 40 ], with less dependence on the examiner’s experience [ 5 , 41 , 42 ], demonstrating the potential for implementation of large-scale screening protocols in the population, to screen for glaucomatous papilla in several evolutionary stages and monitor treatments [ 43 ].…”